1 / 23

Probabilistic analysis

Probabilistic analysis. Wooram Heo. The birthday paradox. How many people must there be in a room before there is a 50% chance that two of them were born on the same day of the year? Index the people with integers 1, 2, …, k : the day of the year on which person i ’s birthday falls

mirit
Télécharger la présentation

Probabilistic analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Probabilistic analysis WooramHeo

  2. The birthday paradox • How many people must there be in a room before there is a 50% chance that two of them were born on the same day of the year? • Index the people with integers 1, 2, …, k • : the day of the year on which person i’s birthday falls • Birthdays are uniformly distributed across the n days

  3. The birthday paradox • Then, the prob. that i’s birthday and j’s birthday both fall on day r is • Thus, the prob. That they both fall on the same day is

  4. The birthday paradox • Pr{at least 2 out of k people having matching birthdays} = 1 – Pr{k people have distinct birthday} • Ai : i’s birthday is different from j’s birthday for all j < i • Bk : Event that k people have distinct birthdays

  5. The birthday paradox • If Bk-1 holds,

  6. The birthday paradox • Prob. That all k birthdays are distinct is at most ½ when • For n = 365, k is bigger than or equal to 23 • Thus, if at least 23 people are in a room, the prob. is at least ½ that two people have the same birthday

  7. Balls and bins • Consider the process of randomly tossing identical balls into b bins, numbered 1, 2, …, b • Tosses are independent. • Prob. that a tossed ball lands in any given bin is 1/b • Ball-tossing process is a sequence of Bernouli(1/b) • Useful for analyzing hashing

  8. Balls and bins • How many balls must one toss until every bin contains at least one ball? • Call a toss in which a ball falls into an empty bin a “hit” • Expected number n of tosses required to get b hits? • Hit can be used partition the n tosses into stages. The ith stage consists of the tosses after the (i- 1)st hit until ith hit.

  9. Balls and bins stage2 stage3 stage b • For each toss during the ith stage, prob. obtaining a hit is (b – i + 1) / b • ni : denote the number of tosses in the ith stage. stage1

  10. Balls and bins • By linearity of expectation,

  11. Streaks • Suppose you flip a fair coin n times. The longest streak of consecutive heads that you expect to see is • Proof consists of two steps; showing and • Aik : the event that a streak of heads of length at least k begins with the ith coin flip. I.e. coin flips i, i + 1, …, i + k – 1 yield only heads.

  12. Streaks • f • Prob. that a streak of heads of length at least begins anywhere is

  13. Streaks • Lj : event that the longest streak of heads has length exactly j • L : the length of the longest streak • E • Events Ljfor j = 0, 1, …, n are disjoint, so the prob. that a streak of heads of length at least begins anywhere is

  14. Streaks

  15. The hiring problem • H • In worst-case, total hiring cost of • What is the expected number of times that manager hires a new office assistant?

  16. The hiring problem • D • D • D • d

  17. The On-line hiring problem • Manager is willing to settle for a candidate who is close to the best, in exchange for hiring exactly once. • After interviewing, either immediately offer the position to the applicant or immediately reject the applicant. • After manager has seen j applicants, he knows which of the j has the highest score, but he does not know whether any of the remaining n – j applicants will receive a higher score.

  18. The On-line hiring problem • H • We wish to determine, for each possible value of k, the probability that we hire the most qualified applicant.

  19. The On-line hiring problem • K • S : event that we succeed in choosing the best-qualified applicant • Si : event that we succeed when the best-qualified applicant is the ith one interviewd. • Since Si are disjoint,

  20. The On-line hiring problem • Bi : event that the best-qualified applicant is in position i. • Oi : event that none of the applicants in position k + 1 through i – 1 chosen. I.e. all of the values score(k + 1) through score(i – 1) must be less than M(k). • Bi and Oiare independent.

  21. The On-line hiring problem • D = 1/n , • D • d

  22. The On-line hiring problem • d • Evaluating these integrals gives us the bounds • To maximize the probability of success, focus on choosing the value of k that maximizes the lower bound on Pr{S}. • By differentiating the expression (k / n) (lnn – lnk) with respect to k, and setting the derivative equal to 0, we will succeed in hiring our best-qualified applicant with prob. at least 1/e.

  23. END

More Related